摘要
文章通过大数据平台搜集到的1990~2012年GDP与第一、二、三产业的增加值,充分利用统计软件R与SPSS,建立了GDP与第一产业增加值的一元线性回归模型 。之后通过相关系数检验得出模型存在异方差性与自相关性,通过一元加权最小二乘估计消除了异方差性,同时通过差分法消除了误差项自相关性,从而对模型进行了改进。又通过GDP与第一、二、三产业建立了多元线性回归模型。由于模型的显著性水平不理想,文章利用逐步回归法对自变量进行了筛选,最终建立了GDP与第二、三产业的线性回归模型 。最终通过所建立的模型,可以通过未来第一二三产业的变化来对GDP增速进行合理的预测。
According to the data about primary, secondary and tertiary industry in 1990-2012 from big data platform, this paper sets up a simple linear regression model ( ) between GDP and the adding value of the primary industry by using the statistical software R and SPSS. Then the test of Correlation Coefficient shows that the model has heteroscedasticity and autocorrelation which were eliminated by “one variable weighted least square estimation” and FDM. So the model is improved. Then this paper uses the GDP and the first, the second, the third industry to establish the multiple linear regression model. Due to the significance of the model is not ideal, this paper uses the stepwise regression method to screen the independent variables and finally establishes the multiple linear regression model ( ) between GDP and the secondary industry and tertiary industry. Finally, based on the established model, we can use the future changes in the primary, secondary and tertiary industries to reasonably forecast the GDP growth rate.
出处
《统计学与应用》
2020年第2期163-171,共9页
Statistical and Application
